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Knowledge Management and Dublin Core

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Knowledge Management and Dublin Core Proc. Int’l Conf. on Dublin Core and Metadata Applications 2009 Knowledge Management and Dublin Core Jon Mason Queensland University of Technology, Australia [email protected] Abstract This paper is concerned with investigating existing and potential scope of Dublin Core metadata in Knowledge Management contexts. Modelling knowledge is identified as a conceptual prerequisite in this investigation, principally for the purpose of clarifying scope prior to identifying the range of tasks associated with organising knowledge. A variety of models is presented and relationships between data, information, and knowledge discussed. It is argued that the two most common modes of organisation, hierarchies and networks, influence the effectiveness and flow of knowledge. Practical perspective is provided by reference to implementations and projects providing evidence of how DC metadata is applied in such contexts. A sense-making model is introduced that can be used as a shorthand reference for identifying useful facets of knowledge that might be described using metadata. Discussion is aimed at presenting this model in a way that both validates current applications and points to potential novel applications. Keywords: KM; knowledge; sense-making; DC; metadata; knowledge management; models; KOS; knowledge organization systems. 1. Introduction The application of Dublin Core (DC) metadata for information management purposes has been taking place ever since the 15 simple DC elements were developed. Its application for industry- specific purposes such as education or government service identification has also been widely adopted, though not without challenges along the way. Knowledge Management (KM) presents an interesting set of challenges for those interested in utilising DC metadata because it can be perceived as a number of different things – as an academic discourse, an organisational intervention, a set of activities that a community of practice might undertake to ensure optimum knowledge flows, or even what an individual might do to maximise the reuse and retrievability of their own knowledge. To underscore this challenge, it is a very interesting exercise just to find a broadly accepted definition of KM that might be both flexible and comprehensive enough to deal with all these scenarios. Although KM first emerged in the 1980s it only began to seriously establish in the mid 1990s when the impact of the Web was just beginning to be felt upon the business world. As such, its early character was biased toward business process improvement from a managerial perspective. The influence of networks upon the way KM has been understood or implemented is something that has only emerged in later discourse (Beerli, et al., 2003; Back, et al., 2006). It has been argued that the fundamental KM problem is all about changing organisational ‘silos’ of activity (manifest in organisational divisions, hierarchical management structures, projects, work teams, documents, and individual workspaces) so that knowledge flows more readily, is shared and leveraged for maximum benefit, and is not pigeonholed nor rendered inaccessible through poor information management practices (Xu and Quaddus, 2005: 382). Such a problem will likely resonate for most people who are employed by organisations. However, while this ‘silo’ problem is clearly evident within hierarchically structured organisations it is also manifest within networks, and it is commonplace for potentially synergistic communities of practice to actually exist more as disconnected ‘islands’. The problem is in fact a deeper one, and 41 2009 Proc. Int’l Conf. on Dublin Core and Metadata Applications is to do with the nature of knowledge and organization—hierarchies and networks are the two most effective forms of organisation that human beings have yet created; both have their place, but it will always be the situational context that will suggest the most effective way to act. Another challenge concerning KM for the DCMI community is that it represents a domain that is arguably more expansive than all other communities currently developing application profiles. Thus, a definition of Knowledge Management that might be useful for the DCMI KM community to consider is presented here: Knowledge Management (KM) finds expression as both an organisational intervention aimed at delivering better efficiencies in the handling of knowledge, and an academic discourse that develops theoretical frameworks and practical techniques for managing the entire knowledge lifecycle from a variety of perspectives: individual, community, and organisational. It can, though doesn’t need to, involve a multiplicity of considerations and tasks and is always influenced by context. 1.1. DCMI KM Community The DCMI Knowledge Management Community was established in mid 2007 as “a forum for individuals and organisations with an interest in the application and use of the Dublin Core standard in knowledge management.” (DCMI, 2007) To some extent this new community provides continuity with issues addressed by the DCMI Global Corporate Circle, which was deactivated in 2007. To date, while there is clearly an interest in this area with over 120 subscribers to the email listserv there is little serious documented discourse. This paper therefore aims to make a contribution by presenting some theoretical framework for consideration, providing examples of how DC metadata is being effectively applied in some Knowledge Management (KM) contexts, and pointing to current limitations of DC in such contexts. 1.2. Overview The following discussion deals with topics on modelling and organising knowledge from a theoretical perspective. A number of scenarios are then presented, indicating how DC metadata has been effectively utilised for KM purposes. A sense-making abstraction is presented as a reference model for identifying prominent facets or pathways of knowledge that will typically need to be considered in KM contexts. This model aims to validate current applications while also pointing to potential novel applications, thereby indicating any limitations with currently available schemas that may need to be overcome. 2. Representing, Modelling, and Managing Knowledge For knowledge to be organised and managed it is necessary to first establish the scope of such an undertaking. This task has been approached by various practitioners and communities of practice in a broad variety of ways. The proceeding discussion is an attempt to summarise some of the more prominent approaches that have relevance for the application of DC metadata. The field of Computer Science provides at least two key (mutually informing) approaches – through formal knowledge representation languages such as Prolog, the Resource Description Framework (RDF), the Web Ontology Language (OWL), and Attempto Controlled English (ACE); or, through conceptual classification into three categories: declarative, procedural, and conditional knowledge. Declarative knowledge is expressed by explicit statements of the kind ‘it is known that’ and typically involves facts or ‘objective’ information; procedural knowledge is typically expressed by statements that represent ‘know-how’; and, conditional knowledge is represented by statements that represent ‘knowing-if’ and/or ‘knowing-why’ (Murphy, 2008). Through recent years of work on developing a robust ‘abstract model’ that can inform future extensibility and application of DC metadata, the Dublin Core Metadata Initiative is now strongly aligned with the knowledge representation capabilities of RDF (Powell, et al., 2007). It is 42 Proc. Int’l Conf. on Dublin Core and Metadata Applications 2009 noteworthy that this work has taken both considerable effort and time and has required ongoing explication through other documents such as the Singapore Framework (Nilsson, et al., 2008). This work has also demonstrated that the goal of defining and using shared semantics is not sufficient for sustainable knowledge sharing in Web environments—for this to occur, semantics must be associated with statements that make clear certain relationships, thereby establishing context through syntax and structural relations. Another approach is to situate knowledge within its relationship to data, information, and wisdom as a value hierarchy and is often depicted as a pyramid (see Figure 1). FIG. 1. Traditional value pyramid representing knowledge However, just as the Internet has rendered geographical boundaries and legal jurisdictions as debatable constructs when it comes to information flows, the boundaries that separate data, information, and knowledge can be very fuzzy and depend upon context. Thus, in the context of the Internet, Figure 1 becomes a very poor representation for the simple reason that data, information, and knowledge become intermeshed. In this environment value can be created through rendering information and knowledge as data and many datasets can either comprise or be extracted from a knowledge-base. In short, this inverts the value-chain depicted by the pyramid (Mason, et al., 2003). Capturing explicit knowledge and organising it as structured information for sharing and reuse is thus one of the powerful features and potentials of the Web. This same recursive property can also be seen with metadata. While it is pragmatic that simple models identify ‘digital assets’ or content as one entity and ‘metadata’ as another (that describes the content) such models can mask deeper complexity. For example, in the case of a repository designed to broker resource discovery,
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